{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68e62cbb",
   "metadata": {},
   "source": [
    "# overview\n",
    "本次实验目标是\n",
    "1. 找到以购买国家为主要分类的同期群\n",
    "2. 计算同期群在不同时间间隔内的保留率retention rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df311639",
   "metadata": {},
   "source": [
    "# load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e6bc97b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "      InvoiceDate  Price  Customer ID         Country       Date  \n",
       "0  2009/12/1 7:45   6.95        13085  United Kingdom  2009/12/1  \n",
       "1  2009/12/1 7:45   6.75        13085  United Kingdom  2009/12/1  \n",
       "2  2009/12/1 7:45   6.75        13085  United Kingdom  2009/12/1  \n",
       "3  2009/12/1 7:45   2.10        13085  United Kingdom  2009/12/1  \n",
       "4  2009/12/1 7:45   1.25        13085  United Kingdom  2009/12/1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings \n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "data_path = \"/Users/collinsliu/learning/jobs/self advance/course/platform_order.csv\"\n",
    "data_df = pd.read_csv(data_path,\n",
    "            encoding='utf-8')\n",
    "data_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4e25f8",
   "metadata": {},
   "source": [
    "# tweak data\n",
    "## 分割取消订单 cancel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60a8f592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>cancel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "\n",
       "      InvoiceDate  Price  Customer ID         Country       Date  cancel  \n",
       "0  2009/12/1 7:45   6.95        13085  United Kingdom  2009/12/1   False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df[\"cancel\"] = data_df[\"Invoice\"].str.startswith(\"C\")\n",
    "data_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94cd80a9",
   "metadata": {},
   "source": [
    "## 添加每条记录 GMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fdc140e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>cancel</th>\n",
       "      <th>GMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009/12/1</td>\n",
       "      <td>False</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "\n",
       "      InvoiceDate  Price  Customer ID         Country       Date  cancel   GMV  \n",
       "0  2009/12/1 7:45   6.95        13085  United Kingdom  2009/12/1   False  83.4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df[\"GMV\"] = data_df[\"Quantity\"] * data_df[\"Price\"]\n",
    "data_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfbf2c9a",
   "metadata": {},
   "source": [
    "## 添加每条记录最初购买时间 first_purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66d1cb5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将Date列转换为datetime格式，方便调用api计算\n",
    "data_df[\"Date\"] = pd.to_datetime(data_df[\"Date\"])\n",
    "type(data_df[\"Date\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f8e990a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>cancel</th>\n",
       "      <th>GMV</th>\n",
       "      <th>first_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>83.4</td>\n",
       "      <td>2009-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "\n",
       "      InvoiceDate  Price  Customer ID         Country       Date  cancel  \\\n",
       "0  2009/12/1 7:45   6.95        13085  United Kingdom 2009-12-01   False   \n",
       "\n",
       "    GMV first_purchase  \n",
       "0  83.4     2009-12-01  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df[\"first_purchase\"] = data_df.groupby(\"Customer ID\")[\"Date\"].transform(\"min\")\n",
    "data_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d6ae753",
   "metadata": {},
   "source": [
    "# check data\n",
    "所有需要计算的条目必须满足以下两个条件\n",
    "1. 不是取消订单\n",
    "2. 订单时间在2011-12-01之前"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8449086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customer num: 5853, \n",
      "invoice_num: 36197, \n",
      "total record: 788315\n"
     ]
    }
   ],
   "source": [
    "data_df = data_df[(data_df[\"cancel\"]==False)\n",
    "                             &(data_df[\"Date\"]<'2011-12-01')]\n",
    "customer_num = data_df[\"Customer ID\"].nunique()\n",
    "invoice_num = data_df[\"Invoice\"].nunique()\n",
    "ttl_record = data_df[\"Invoice\"].count()\n",
    "print(\"customer num: {}, \\ninvoice_num: {}, \\ntotal record: {}\" \\\n",
    "      .format(customer_num, invoice_num,ttl_record))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7467ea81",
   "metadata": {},
   "source": [
    "# cohort analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c67ef3",
   "metadata": {},
   "source": [
    "## define cohort group - row\n",
    "1. 我们采取订货国家作为分组依据\n",
    "2. 当遇到一个顾客从不同国家订货时，我们选择GMV最高的国家作为标注国家\n",
    "3. 当遇到一个国家数据量较少时，对这些国家进行合并处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a4668d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_c = data_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a99c7ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
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       "      <th>cancel</th>\n",
       "      <th>GMV</th>\n",
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       "      <th>cohort_country</th>\n",
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       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>83.4</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>100.8</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009/12/1 7:45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "      InvoiceDate  Price  Customer ID         Country       Date  cancel  \\\n",
       "0  2009/12/1 7:45   6.95        13085  United Kingdom 2009-12-01   False   \n",
       "1  2009/12/1 7:45   6.75        13085  United Kingdom 2009-12-01   False   \n",
       "2  2009/12/1 7:45   6.75        13085  United Kingdom 2009-12-01   False   \n",
       "3  2009/12/1 7:45   2.10        13085  United Kingdom 2009-12-01   False   \n",
       "4  2009/12/1 7:45   1.25        13085  United Kingdom 2009-12-01   False   \n",
       "\n",
       "     GMV first_purchase  cohort_country  \n",
       "0   83.4     2009-12-01  United Kingdom  \n",
       "1   81.0     2009-12-01  United Kingdom  \n",
       "2   81.0     2009-12-01  United Kingdom  \n",
       "3  100.8     2009-12-01  United Kingdom  \n",
       "4   30.0     2009-12-01  United Kingdom  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df = data_c.groupby([\"Customer ID\",\"Country\"])[\"GMV\"] \\\n",
    "    .sum().reset_index()\n",
    "country_df_max = country_df.sort_values([\"Customer ID\",\"GMV\"]) \\\n",
    "    .groupby([\"Customer ID\"])[[\"Country\",\"GMV\"]].first().reset_index() \\\n",
    "    .rename(columns={\"Country\":\"cohort_country\"})\n",
    "\n",
    "data_c = data_c.merge(country_df_max,left_on=\"Customer ID\",\n",
    "             right_on=\"Customer ID\", how=\"left\") \\\n",
    "            .rename(columns={\"GMV_x\":\"GMV\"}) \\\n",
    "            .drop(columns=[\"GMV_y\"])\n",
    "data_c.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9ffbb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>cancel</th>\n",
       "      <th>GMV</th>\n",
       "      <th>first_purchase</th>\n",
       "      <th>cohort_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>276040</th>\n",
       "      <td>524010</td>\n",
       "      <td>21990</td>\n",
       "      <td>MODERN FLORAL STATIONERY SET</td>\n",
       "      <td>48</td>\n",
       "      <td>2010/9/27 9:37</td>\n",
       "      <td>2.55</td>\n",
       "      <td>12449</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>False</td>\n",
       "      <td>122.40</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276041</th>\n",
       "      <td>524010</td>\n",
       "      <td>22035</td>\n",
       "      <td>VINTAGE CARAVAN GREETING CARD</td>\n",
       "      <td>72</td>\n",
       "      <td>2010/9/27 9:37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>12449</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>False</td>\n",
       "      <td>25.92</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276042</th>\n",
       "      <td>524010</td>\n",
       "      <td>22417</td>\n",
       "      <td>PACK OF 60 SPACEBOY CAKE CASES</td>\n",
       "      <td>72</td>\n",
       "      <td>2010/9/27 9:37</td>\n",
       "      <td>0.55</td>\n",
       "      <td>12449</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>False</td>\n",
       "      <td>39.60</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276043</th>\n",
       "      <td>524010</td>\n",
       "      <td>21212</td>\n",
       "      <td>PACK OF 72 RETRO SPOT CAKE CASES</td>\n",
       "      <td>120</td>\n",
       "      <td>2010/9/27 9:37</td>\n",
       "      <td>0.42</td>\n",
       "      <td>12449</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>False</td>\n",
       "      <td>50.40</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276044</th>\n",
       "      <td>524010</td>\n",
       "      <td>22755</td>\n",
       "      <td>SMALL PURPLE BABUSHKA NOTEBOOK</td>\n",
       "      <td>36</td>\n",
       "      <td>2010/9/27 9:37</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12449</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>False</td>\n",
       "      <td>30.60</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758994</th>\n",
       "      <td>577046</td>\n",
       "      <td>23247</td>\n",
       "      <td>BISCUIT TIN 50'S CHRISTMAS</td>\n",
       "      <td>6</td>\n",
       "      <td>2011/11/17 13:46</td>\n",
       "      <td>2.89</td>\n",
       "      <td>12449</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>False</td>\n",
       "      <td>17.34</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758995</th>\n",
       "      <td>577046</td>\n",
       "      <td>21906</td>\n",
       "      <td>PHARMACIE FIRST AID TIN</td>\n",
       "      <td>2</td>\n",
       "      <td>2011/11/17 13:46</td>\n",
       "      <td>6.75</td>\n",
       "      <td>12449</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>False</td>\n",
       "      <td>13.50</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758996</th>\n",
       "      <td>577046</td>\n",
       "      <td>22494</td>\n",
       "      <td>EMERGENCY FIRST AID TIN</td>\n",
       "      <td>12</td>\n",
       "      <td>2011/11/17 13:46</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12449</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>False</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758997</th>\n",
       "      <td>577046</td>\n",
       "      <td>79066K</td>\n",
       "      <td>RETRO MOD TRAY</td>\n",
       "      <td>10</td>\n",
       "      <td>2011/11/17 13:46</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12449</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>False</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758998</th>\n",
       "      <td>577046</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>7</td>\n",
       "      <td>2011/11/17 13:46</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12449</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>False</td>\n",
       "      <td>126.00</td>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode                       Description  Quantity  \\\n",
       "276040  524010     21990      MODERN FLORAL STATIONERY SET        48   \n",
       "276041  524010     22035    VINTAGE CARAVAN GREETING CARD         72   \n",
       "276042  524010     22417    PACK OF 60 SPACEBOY CAKE CASES        72   \n",
       "276043  524010     21212  PACK OF 72 RETRO SPOT CAKE CASES       120   \n",
       "276044  524010     22755   SMALL PURPLE BABUSHKA NOTEBOOK         36   \n",
       "...        ...       ...                               ...       ...   \n",
       "758994  577046     23247        BISCUIT TIN 50'S CHRISTMAS         6   \n",
       "758995  577046     21906           PHARMACIE FIRST AID TIN         2   \n",
       "758996  577046     22494          EMERGENCY FIRST AID TIN         12   \n",
       "758997  577046    79066K                    RETRO MOD TRAY        10   \n",
       "758998  577046      POST                           POSTAGE         7   \n",
       "\n",
       "             InvoiceDate  Price  Customer ID  Country       Date  cancel  \\\n",
       "276040    2010/9/27 9:37   2.55        12449  Denmark 2010-09-27   False   \n",
       "276041    2010/9/27 9:37   0.36        12449  Denmark 2010-09-27   False   \n",
       "276042    2010/9/27 9:37   0.55        12449  Denmark 2010-09-27   False   \n",
       "276043    2010/9/27 9:37   0.42        12449  Denmark 2010-09-27   False   \n",
       "276044    2010/9/27 9:37   0.85        12449  Denmark 2010-09-27   False   \n",
       "...                  ...    ...          ...      ...        ...     ...   \n",
       "758994  2011/11/17 13:46   2.89        12449  Belgium 2011-11-17   False   \n",
       "758995  2011/11/17 13:46   6.75        12449  Belgium 2011-11-17   False   \n",
       "758996  2011/11/17 13:46   1.25        12449  Belgium 2011-11-17   False   \n",
       "758997  2011/11/17 13:46   0.85        12449  Belgium 2011-11-17   False   \n",
       "758998  2011/11/17 13:46  18.00        12449  Belgium 2011-11-17   False   \n",
       "\n",
       "           GMV first_purchase cohort_country  \n",
       "276040  122.40     2010-09-27        Denmark  \n",
       "276041   25.92     2010-09-27        Denmark  \n",
       "276042   39.60     2010-09-27        Denmark  \n",
       "276043   50.40     2010-09-27        Denmark  \n",
       "276044   30.60     2010-09-27        Denmark  \n",
       "...        ...            ...            ...  \n",
       "758994   17.34     2010-09-27        Denmark  \n",
       "758995   13.50     2010-09-27        Denmark  \n",
       "758996   15.00     2010-09-27        Denmark  \n",
       "758997    8.50     2010-09-27        Denmark  \n",
       "758998  126.00     2010-09-27        Denmark  \n",
       "\n",
       "[201 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c[data_c[\"Customer ID\"] == 12449.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34cbfe43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cohort_country\n",
       "West Indies                1\n",
       "Thailand                   1\n",
       "Singapore                  1\n",
       "Saudi Arabia               1\n",
       "Nigeria                    1\n",
       "Lithuania                  1\n",
       "Czech Republic             1\n",
       "Lebanon                    1\n",
       "Iceland                    1\n",
       "European Community         1\n",
       "RSA                        2\n",
       "Malta                      2\n",
       "Korea                      2\n",
       "Bahrain                    2\n",
       "Brazil                     2\n",
       "United Arab Emirates       4\n",
       "Greece                     4\n",
       "Israel                     4\n",
       "EIRE                       5\n",
       "Canada                     5\n",
       "Unspecified                6\n",
       "Poland                     6\n",
       "USA                        8\n",
       "Denmark                    9\n",
       "Cyprus                     9\n",
       "Japan                     10\n",
       "Norway                    13\n",
       "Austria                   13\n",
       "Australia                 13\n",
       "Finland                   13\n",
       "Channel Islands           13\n",
       "Italy                     17\n",
       "Sweden                    19\n",
       "Switzerland               21\n",
       "Netherlands               22\n",
       "Portugal                  24\n",
       "Belgium                   26\n",
       "Spain                     40\n",
       "France                    92\n",
       "Germany                  106\n",
       "United Kingdom          5331\n",
       "Name: Customer ID, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c.groupby(\"cohort_country\")[\"Customer ID\"].nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d71d2d7",
   "metadata": {},
   "source": [
    "### analysis\n",
    "从上述分类结果中我们可以看出，如果按照自然分类，除去前三名外剩余的国家由于样本数量太小将不具备统计学意义，<br>因此我们将数据区分为前三名和其他"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fba3f092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_group\n",
       "France       92\n",
       "Germany     106\n",
       "Others      324\n",
       "UK         5331\n",
       "Name: Customer ID, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c[\"country_group\"] = data_c[\"cohort_country\"] \\\n",
    "        .apply(lambda row: \"UK\" if row==\"United Kingdom\" \n",
    "                   else (\"Germany\" if row ==\"Germany\" \n",
    "                   else (\"France\" if row ==\"France\" \n",
    "                   else \"Others\")))\n",
    "data_c.groupby(\"country_group\")[\"Customer ID\"].nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5eeb84",
   "metadata": {},
   "source": [
    "## define time - column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d7c5abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_c[\"cohort_index\"] = (data_c[\"Date\"].dt.year  - data_c[\"first_purchase\"].dt.year) *12 \\\n",
    "                         + data_c[\"Date\"].dt.month  - data_c[\"first_purchase\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "acf41b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cohort_index</th>\n",
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       "      <th>23</th>\n",
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       "      <th>country_group</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>103</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
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       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>316</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>5237</td>\n",
       "      <td>1213</td>\n",
       "      <td>1165</td>\n",
       "      <td>1203</td>\n",
       "      <td>1082</td>\n",
       "      <td>1017</td>\n",
       "      <td>1003</td>\n",
       "      <td>955</td>\n",
       "      <td>863</td>\n",
       "      <td>822</td>\n",
       "      <td>...</td>\n",
       "      <td>584</td>\n",
       "      <td>602</td>\n",
       "      <td>578</td>\n",
       "      <td>597</td>\n",
       "      <td>509</td>\n",
       "      <td>515</td>\n",
       "      <td>486</td>\n",
       "      <td>411</td>\n",
       "      <td>361</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_index     0     1     2     3     4     5     6    7    8    9   ...  \\\n",
       "country_group                                                           ...   \n",
       "France           89    22    27    22    16    17    20   16   11   22  ...   \n",
       "Germany         103    26    35    22    27    20    24   23   21   22  ...   \n",
       "Others          316    65    55    62    53    47    50   48   39   37  ...   \n",
       "UK             5237  1213  1165  1203  1082  1017  1003  955  863  822  ...   \n",
       "\n",
       "cohort_index    14   15   16   17   18   19   20   21   22   23  \n",
       "country_group                                                    \n",
       "France          14   17   11   10   18   10   11   12    7   10  \n",
       "Germany         15   20   17   13   14   17   12   13   10    7  \n",
       "Others          27   33   25   16   25   19   16   18   15   12  \n",
       "UK             584  602  578  597  509  515  486  411  361  385  \n",
       "\n",
       "[4 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_raw = data_c.groupby([\"country_group\",\"cohort_index\"])[\"Customer ID\"] \\\n",
    "                   .nunique().reset_index()\n",
    "cohort_matrix = cohort_raw.pivot(index=\"country_group\", columns=\"cohort_index\",values=\"Customer ID\")\n",
    "cohort_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819363ad",
   "metadata": {},
   "source": [
    "## define churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e137259e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort_index</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>24.7%</td>\n",
       "      <td>30.3%</td>\n",
       "      <td>24.7%</td>\n",
       "      <td>18.0%</td>\n",
       "      <td>19.1%</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>18.0%</td>\n",
       "      <td>12.4%</td>\n",
       "      <td>24.7%</td>\n",
       "      <td>20.2%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7%</td>\n",
       "      <td>19.1%</td>\n",
       "      <td>12.4%</td>\n",
       "      <td>11.2%</td>\n",
       "      <td>20.2%</td>\n",
       "      <td>11.2%</td>\n",
       "      <td>12.4%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>11.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>25.2%</td>\n",
       "      <td>34.0%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>26.2%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>23.3%</td>\n",
       "      <td>22.3%</td>\n",
       "      <td>20.4%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>23.3%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.6%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>12.6%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>12.6%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>6.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>20.6%</td>\n",
       "      <td>17.4%</td>\n",
       "      <td>19.6%</td>\n",
       "      <td>16.8%</td>\n",
       "      <td>14.9%</td>\n",
       "      <td>15.8%</td>\n",
       "      <td>15.2%</td>\n",
       "      <td>12.3%</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>11.1%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>10.4%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>5.1%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>5.1%</td>\n",
       "      <td>5.7%</td>\n",
       "      <td>4.7%</td>\n",
       "      <td>3.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>23.2%</td>\n",
       "      <td>22.2%</td>\n",
       "      <td>23.0%</td>\n",
       "      <td>20.7%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>18.2%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>15.7%</td>\n",
       "      <td>15.5%</td>\n",
       "      <td>...</td>\n",
       "      <td>11.2%</td>\n",
       "      <td>11.5%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>11.4%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>6.9%</td>\n",
       "      <td>7.4%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_index      1      2      3      4      5      6      7      8      9   \\\n",
       "country_group                                                                  \n",
       "France         24.7%  30.3%  24.7%  18.0%  19.1%  22.5%  18.0%  12.4%  24.7%   \n",
       "Germany        25.2%  34.0%  21.4%  26.2%  19.4%  23.3%  22.3%  20.4%  21.4%   \n",
       "Others         20.6%  17.4%  19.6%  16.8%  14.9%  15.8%  15.2%  12.3%  11.7%   \n",
       "UK             23.2%  22.2%  23.0%  20.7%  19.4%  19.2%  18.2%  16.5%  15.7%   \n",
       "\n",
       "cohort_index      10  ...     14     15     16     17     18     19     20  \\\n",
       "country_group         ...                                                    \n",
       "France         20.2%  ...  15.7%  19.1%  12.4%  11.2%  20.2%  11.2%  12.4%   \n",
       "Germany        23.3%  ...  14.6%  19.4%  16.5%  12.6%  13.6%  16.5%  11.7%   \n",
       "Others         11.1%  ...   8.5%  10.4%   7.9%   5.1%   7.9%   6.0%   5.1%   \n",
       "UK             15.5%  ...  11.2%  11.5%  11.0%  11.4%   9.7%   9.8%   9.3%   \n",
       "\n",
       "cohort_index      21    22     23  \n",
       "country_group                      \n",
       "France         13.5%  7.9%  11.2%  \n",
       "Germany        12.6%  9.7%   6.8%  \n",
       "Others          5.7%  4.7%   3.8%  \n",
       "UK              7.8%  6.9%   7.4%  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_size = cohort_matrix.iloc[:,0]\n",
    "cohort_rr = cohort_matrix.iloc[:,1:].divide(cohort_size,axis=0)\n",
    "cohort_rr.applymap(lambda row: \"{0:.1f}%\".format(row*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4d6438",
   "metadata": {},
   "source": [
    "### calculate retension rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca2e0e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_group\n",
       "France     0.174890\n",
       "Germany    0.188265\n",
       "Others     0.111860\n",
       "UK         0.146292\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_rr.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "badecff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cohort_index'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cohort_rr.T.plot.line(figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a21ae885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>country_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13085</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13078</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15362</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>18102</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>12682</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785477</th>\n",
       "      <td>13521</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785742</th>\n",
       "      <td>14349</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787199</th>\n",
       "      <td>12966</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787410</th>\n",
       "      <td>15060</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787776</th>\n",
       "      <td>17911</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5853 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Customer ID country_group\n",
       "0             13085            UK\n",
       "12            13078            UK\n",
       "31            15362            UK\n",
       "54            18102            UK\n",
       "71            12682        France\n",
       "...             ...           ...\n",
       "785477        13521            UK\n",
       "785742        14349            UK\n",
       "787199        12966            UK\n",
       "787410        15060            UK\n",
       "787776        17911            UK\n",
       "\n",
       "[5853 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c[[\"Customer ID\",\"country_group\"]].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66119a4",
   "metadata": {},
   "source": [
    "### 计算每个用户两次购买间隔长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "230137fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>country_group</th>\n",
       "      <th>cohort_index</th>\n",
       "      <th>invoice_year</th>\n",
       "      <th>invoice_month</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>GMV</th>\n",
       "      <th>cohort_lead</th>\n",
       "      <th>trans_gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>113.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>90.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>142.31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>611.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1423.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>475.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>636.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>382.52</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID country_group  cohort_index  invoice_year  invoice_month  \\\n",
       "0        12346            UK             0          2009             12   \n",
       "1        12346            UK             1          2010              1   \n",
       "2        12346            UK             3          2010              3   \n",
       "3        12346            UK             6          2010              6   \n",
       "4        12346            UK            13          2011              1   \n",
       "5        12347        Others             0          2010             10   \n",
       "6        12347        Others             2          2010             12   \n",
       "7        12347        Others             3          2011              1   \n",
       "8        12347        Others             6          2011              4   \n",
       "9        12347        Others             8          2011              6   \n",
       "\n",
       "   Invoice       GMV  cohort_lead  trans_gap  \n",
       "0        5    113.50          NaN        NaN  \n",
       "1        4     90.00          0.0        1.0  \n",
       "2        1     27.05          1.0        2.0  \n",
       "3        1    142.31          3.0        3.0  \n",
       "4        1  77183.60          6.0        7.0  \n",
       "5        1    611.53          NaN        NaN  \n",
       "6        1   1423.58          0.0        2.0  \n",
       "7        1    475.39          2.0        1.0  \n",
       "8        1    636.25          3.0        3.0  \n",
       "9        1    382.52          6.0        2.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c[\"invoice_year\"] = data_c[\"Date\"].dt.year\n",
    "data_c[\"invoice_month\"] = data_c[\"Date\"].dt.month\n",
    "data_d = data_c.sort_values([\"Customer ID\",\"cohort_index\"]) \\\n",
    "      .groupby([\"Customer ID\",\"country_group\",\"cohort_index\",\"invoice_year\",\"invoice_month\"]) \\\n",
    "      .agg({\"Invoice\":\"nunique\", \"GMV\":\"sum\"}) \\\n",
    "      .reset_index()\n",
    "data_d[\"cohort_lead\"] = data_d.groupby([\"Customer ID\"])[\"cohort_index\"].shift(1)\n",
    "data_d[\"trans_gap\"] = data_d[\"cohort_index\"] - data_d[\"cohort_lead\"]\n",
    "data_d.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a751cb45",
   "metadata": {},
   "source": [
    "### 计算每个用户的retention time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c0cffead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>country_group</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>GMV</th>\n",
       "      <th>trans_gap</th>\n",
       "      <th>duration</th>\n",
       "      <th>last_gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>12</td>\n",
       "      <td>77556.46</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>7</td>\n",
       "      <td>5408.50</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>Others</td>\n",
       "      <td>5</td>\n",
       "      <td>2019.40</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>Others</td>\n",
       "      <td>4</td>\n",
       "      <td>4428.69</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12351</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "      <td>300.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12352</td>\n",
       "      <td>Others</td>\n",
       "      <td>10</td>\n",
       "      <td>2849.84</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12353</td>\n",
       "      <td>Others</td>\n",
       "      <td>2</td>\n",
       "      <td>406.76</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12354</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "      <td>1079.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12355</td>\n",
       "      <td>Others</td>\n",
       "      <td>2</td>\n",
       "      <td>947.61</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID country_group  Invoice       GMV  trans_gap  duration  last_gap\n",
       "0        12346            UK       12  77556.46   3.250000        13        11\n",
       "1        12347        Others        7   5408.50   2.000000        12         2\n",
       "2        12348        Others        5   2019.40   3.000000        12         3\n",
       "3        12349        Others        4   4428.69   6.333333        23         1\n",
       "4        12350        Others        1    334.40        NaN         0        10\n",
       "5        12351        Others        1    300.93        NaN         0        13\n",
       "6        12352        Others       10   2849.84   3.000000        12         1\n",
       "7        12353        Others        2    406.76   7.000000         7         7\n",
       "8        12354        Others        1   1079.40        NaN         0         8\n",
       "9        12355        Others        2    947.61  12.000000        12         7"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_d[\"last_gap\"] = (2011-data_d[\"invoice_year\"])*12 + (12-data_d[\"invoice_month\"])\n",
    "data_d2 = data_d.groupby([\"Customer ID\",\"country_group\"]) \\\n",
    "                .agg({\"Invoice\":\"sum\",\n",
    "                      \"GMV\":\"sum\",\n",
    "                      \"trans_gap\":\"mean\",\n",
    "                      \"cohort_index\":\"max\",\n",
    "                      \"last_gap\":\"min\"}) \\\n",
    "                .reset_index() \\\n",
    "                .rename(columns={\"cohort_index\":\"duration\"})\n",
    "data_d2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aebc3d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4036.000000\n",
       "mean        4.044379\n",
       "std         3.369243\n",
       "min         1.000000\n",
       "25%         1.818182\n",
       "50%         3.000000\n",
       "75%         5.000000\n",
       "max        23.000000\n",
       "Name: trans_gap, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_d2[\"trans_gap\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa52fcb",
   "metadata": {},
   "source": [
    "## tag customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fc47761a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>country_group</th>\n",
       "      <th>Invoice</th>\n",
       "      <th>GMV</th>\n",
       "      <th>trans_gap</th>\n",
       "      <th>duration</th>\n",
       "      <th>last_gap</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>UK</td>\n",
       "      <td>12</td>\n",
       "      <td>77556.46</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>Others</td>\n",
       "      <td>7</td>\n",
       "      <td>5408.50</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>Others</td>\n",
       "      <td>5</td>\n",
       "      <td>2019.40</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>Others</td>\n",
       "      <td>4</td>\n",
       "      <td>4428.69</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>Others</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID country_group  Invoice       GMV  trans_gap  duration  \\\n",
       "0        12346            UK       12  77556.46   3.250000        13   \n",
       "1        12347        Others        7   5408.50   2.000000        12   \n",
       "2        12348        Others        5   2019.40   3.000000        12   \n",
       "3        12349        Others        4   4428.69   6.333333        23   \n",
       "4        12350        Others        1    334.40        NaN         0   \n",
       "\n",
       "   last_gap  churn  \n",
       "0        11      1  \n",
       "1         2      0  \n",
       "2         3      0  \n",
       "3         1      0  \n",
       "4        10      1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_d2[\"churn\"] = data_d2[\"last_gap\"].apply(lambda row: 1 if row>5 else 0)\n",
    "data_d2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "268a9147",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_d2.to_csv(\"country.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68f1de6",
   "metadata": {},
   "source": [
    "### 计算所有群流失率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cc9bf1bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.560567\n",
       "1    0.439433\n",
       "Name: churn, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_d2[\"churn\"].value_counts()/data_d2[\"Customer ID\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd39c41",
   "metadata": {},
   "source": [
    "### 计算一下不同同期群的流失率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "762c565c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_group</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>churn</th>\n",
       "      <th>churn_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>92</td>\n",
       "      <td>25</td>\n",
       "      <td>0.271739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>106</td>\n",
       "      <td>30</td>\n",
       "      <td>0.283019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Others</td>\n",
       "      <td>324</td>\n",
       "      <td>148</td>\n",
       "      <td>0.456790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UK</td>\n",
       "      <td>5331</td>\n",
       "      <td>2369</td>\n",
       "      <td>0.444382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_group  Customer ID  churn  churn_rate\n",
       "0        France           92     25    0.271739\n",
       "1       Germany          106     30    0.283019\n",
       "2        Others          324    148    0.456790\n",
       "3            UK         5331   2369    0.444382"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_d3 = data_d2.groupby('country_group') \\\n",
    "            .agg({\"Customer ID\":\"nunique\",\n",
    "                  \"churn\":\"sum\"}) \\\n",
    "            .reset_index()\n",
    "data_d3[\"churn_rate\"] = data_d3[\"churn\"] / data_d3[\"Customer ID\"]\n",
    "data_d3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82c46f93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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